File size: 3,283 Bytes
fc0f7bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | # Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import numpy as np
import pandas as pd
_MESSAGE_X_NONE = "Must supply X"
_MESSAGE_Y_NONE = "Must supply y"
_MESSAGE_X_Y_ROWS = "X and y must have same number of rows"
_MESSAGE_X_SENSITIVE_ROWS = "X and the sensitive features must have same number of rows"
_KW_SENSITIVE_FEATURES = "sensitive_features"
def _validate_and_reformat_reductions_input(X, y, enforce_binary_sensitive_feature=False,
**kwargs):
if X is None:
raise ValueError(_MESSAGE_X_NONE)
if y is None:
raise ValueError(_MESSAGE_Y_NONE)
if _KW_SENSITIVE_FEATURES not in kwargs:
msg = "Must specify {0} (for now)".format(_KW_SENSITIVE_FEATURES)
raise RuntimeError(msg)
# Extract the target attribute
sensitive_features_vector = _make_vector(kwargs[_KW_SENSITIVE_FEATURES],
_KW_SENSITIVE_FEATURES)
if enforce_binary_sensitive_feature:
unique_labels = np.unique(sensitive_features_vector)
if len(unique_labels) > 2:
raise RuntimeError("Sensitive features contain more than two unique values")
# Extract the Y values
y_vector = _make_vector(y, "y")
X_rows, _ = _get_matrix_shape(X, "X")
if X_rows != y_vector.shape[0]:
raise RuntimeError(_MESSAGE_X_Y_ROWS)
if X_rows != sensitive_features_vector.shape[0]:
raise RuntimeError(_MESSAGE_X_SENSITIVE_ROWS)
return pd.DataFrame(X), y_vector, sensitive_features_vector
def _make_vector(formless, formless_name):
formed_vector = None
if isinstance(formless, list):
formed_vector = pd.Series(formless)
elif isinstance(formless, pd.DataFrame):
if len(formless.columns) == 1:
formed_vector = formless.iloc[:, 0]
else:
msgfmt = "{0} is a DataFrame with more than one column"
raise RuntimeError(msgfmt.format(formless_name))
elif isinstance(formless, pd.Series):
formed_vector = formless
elif isinstance(formless, np.ndarray):
if len(formless.shape) == 1:
formed_vector = pd.Series(formless)
elif len(formless.shape) == 2 and formless.shape[1] == 1:
formed_vector = pd.Series(formless[:, 0])
else:
msgfmt = "{0} is an ndarray with more than one column"
raise RuntimeError(msgfmt.format(formless_name))
else:
msgfmt = "{0} not an ndarray, Series or DataFrame"
raise RuntimeError(msgfmt.format(formless_name))
return formed_vector
def _get_matrix_shape(formless, formless_name):
num_rows = -1
num_cols = -1
if isinstance(formless, pd.DataFrame):
num_cols = len(formless.columns)
num_rows = len(formless.index)
elif isinstance(formless, np.ndarray):
if len(formless.shape) == 2:
num_rows = formless.shape[0]
num_cols = formless.shape[1]
else:
msgfmt = "{0} is an ndarray which is not 2D"
raise RuntimeError(msgfmt.format(formless_name))
else:
msgfmt = "{0} not an ndarray or DataFrame"
raise RuntimeError(msgfmt.format(formless_name))
return num_rows, num_cols
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